


'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """Wav2Vec2FeatureExtractor"""
							A_				: str       =    """AutoTokenizer"""






							def __init__(       self       ,   __snake_case       ,   __snake_case    ):
											super().__init__(__snake_case       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.feature_extractor
											_SCREAMING_SNAKE_CASE					:			int							= False






							@classmethod
							def UpperCAmelCase_      (       cls       ,   __snake_case       ,   **__snake_case    ):
											try:
															return super().from_pretrained(__snake_case       ,   **__snake_case    )
											except OSError:
															warnings.warn(
															    f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
															    """ include a `tokenizer_class` attribute is deprecated and will be """
															    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
															    """ attribute to either your `config.json` or `tokenizer_config.json` """
															    """file to suppress this warning: """       ,   __snake_case       ,   )

															_SCREAMING_SNAKE_CASE					:			Tuple							= WavaVecaFeatureExtractor.from_pretrained(__snake_case       ,   **__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Dict							= WavaVecaCTCTokenizer.from_pretrained(__snake_case       ,   **__snake_case    )

															return cls(feature_extractor=__snake_case       ,   tokenizer=__snake_case    )






							def __call__(       self       ,   *__snake_case       ,   **__snake_case    ):
											# For backward compatibility
											if self._in_target_context_manager:
															return self.current_processor(*__snake_case       ,   **__snake_case    )

											if "raw_speech" in kwargs:
															warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""    )
															_SCREAMING_SNAKE_CASE					:			Any							= kwargs.pop("""raw_speech"""    )
											else:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= kwargs.pop("""audio"""       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= kwargs.pop("""sampling_rate"""       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= kwargs.pop("""text"""       ,   __snake_case    )
											if len(__snake_case    ) > 0:
															_SCREAMING_SNAKE_CASE					:			Any							= args[0]
															_SCREAMING_SNAKE_CASE					:			Dict							= args[1:]

											if audio is None and text is None:
															raise ValueError("""You need to specify either an `audio` or `text` input to process."""    )

											if audio is not None:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= self.feature_extractor(__snake_case       ,   *__snake_case       ,   sampling_rate=__snake_case       ,   **__snake_case    )
											if text is not None:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.tokenizer(__snake_case       ,   **__snake_case    )

											if text is None:
															return inputs
											elif audio is None:
															return encodings
											else:
															_SCREAMING_SNAKE_CASE					:			List[str]							= encodings["""input_ids"""]
															return inputs






							def UpperCAmelCase_      (       self       ,   *__snake_case       ,   **__snake_case    ):
											# For backward compatibility
											if self._in_target_context_manager:
															return self.current_processor.pad(*__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= kwargs.pop("""input_features"""       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= kwargs.pop("""labels"""       ,   __snake_case    )
											if len(__snake_case    ) > 0:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= args[0]
															_SCREAMING_SNAKE_CASE					:			int							= args[1:]

											if input_features is not None:
															_SCREAMING_SNAKE_CASE					:			str							= self.feature_extractor.pad(__snake_case       ,   *__snake_case       ,   **__snake_case    )
											if labels is not None:
															_SCREAMING_SNAKE_CASE					:			str							= self.tokenizer.pad(__snake_case       ,   **__snake_case    )

											if labels is None:
															return input_features
											elif input_features is None:
															return labels
											else:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= labels["""input_ids"""]
															return input_features






							def UpperCAmelCase_      (       self       ,   *__snake_case       ,   **__snake_case    ):
											return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   *__snake_case       ,   **__snake_case    ):
											return self.tokenizer.decode(*__snake_case       ,   **__snake_case    )






							@contextmanager
							def UpperCAmelCase_      (       self    ):
											warnings.warn(
											    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
											    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
											    """your audio inputs, or in a separate call."""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= True
											_SCREAMING_SNAKE_CASE					:			int							= self.tokenizer
											yield
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.feature_extractor
											_SCREAMING_SNAKE_CASE					:			Dict							= False








'''simple docstring'''


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


UpperCAmelCase_						:  List[Any]				  =						'sshleifer/bart-tiny-random'
UpperCAmelCase_						:  Tuple				  =						'patrickvonplaten/t5-tiny-random'





@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoConfig.from_pretrained(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.num_hidden_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			str							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   self.teacher_config.encoder_layers    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											with self.assertRaises(__snake_case    ):
															create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=__snake_case       ,   d=__snake_case    )




